{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"7a8ffe33-92ff-4c24-87e9-5041839c4e5e\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.0.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      Bokeh.$(\"#7a8ffe33-92ff-4c24-87e9-5041839c4e5e\").text(\"BokehJS successfully loaded\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i](window.Bokeh);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Modules for data processing\n",
    "import numpy as np\n",
    "import os\n",
    "from netCDF4 import Dataset\n",
    "import scipy.io as sio\n",
    "import xlrd\n",
    "\n",
    "# Modules for plotting\n",
    "from ipywidgets import *\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import bokeh.io\n",
    "import bokeh.mpl\n",
    "import bokeh.plotting\n",
    "bokeh.io.output_notebook()\n",
    "import seaborn as sns\n",
    "sns.set_style(style='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Option 1: Load data from an Excel file\n",
    "<pre>\n",
    "workbook = xlrd.open_workbook(<span style=\"color:#cc7a00\"><b>'EXCEL_FILE.xlsx'</b></span>)\n",
    "sheet_names = workbook.sheet_names()\n",
    "sheet = workbook.sheet_by_name(sheet_names[i])\n",
    "</pre>\n",
    "\n",
    "#### Option 2: Load data from a Matlab file\n",
    "<pre>\n",
    "matfile  = sio.loadmat(<span style=\"color:#cc7a00\"><b>'MATLAB_FILE.mat'</b></span>)\n",
    "data = matfile[<span style=\"color:#cc7a00\"><b>'VARIABLE_TOPOGRAPHY'</b></span>]\n",
    "</pre>\n",
    "\n",
    "#### Option 3: Load data from a netCDF file\n",
    "<pre>\n",
    "nc_fid = Dataset(<span style=\"color:#cc7a00\"><b>'NETCDF_FILE.nc'</b></span>, 'r')\n",
    "data = nc_fid.variables[<span style=\"color:#cc7a00\"><b>VARIABLE_TOPOGRAPHY</b></span>][:]\n",
    "</pre>\n",
    "\n",
    "#### Run the command below to create forcing for Dhara model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load matlab file\n",
    "matfile  = sio.loadmat('flux_data.mat')\n",
    "\n",
    "# Load variables\n",
    "LAI_in = matfile['LAI_in']\n",
    "LAD_in = np.array([\n",
    "        0.0010, 0.0337, 0.0661, 0.0610, 0.0575,\n",
    "        0.0575, 0.0660, 0.0644, 0.0629, 0.0720,\n",
    "        0.0891, 0.1110, 0.1395, 0.0945, 0.0250])\n",
    "rootfr_in = np.array([\n",
    "        0.8100, 0.1000, 0.0400, 0.0200, 0.0130, \n",
    "        0.0070, 0.0040, 0.0030, 0.0020, 0.0001, \n",
    "        0.0001, 0.0001, 0.0001, 0.0001, 0.0001])\n",
    "\n",
    "nl_can_in    = len(LAD_in)\n",
    "nl_soil_in   = len(rootfr_in)\n",
    "num_steps_in = len(LAI_in)\n",
    "\n",
    "# Check/create folder and topography file name\n",
    "folder = 'data'\n",
    "filename_output = 'soybean.nc'\n",
    "if not os.path.exists(folder):\n",
    "    os.makedirs(folder)\n",
    "\n",
    "################################################################\n",
    "# DO NOT CHANGE BELOW THIS LINE --------------------------------\n",
    "################################################################\n",
    "\n",
    "plant = Dataset(folder + '/' + filename_output, 'w', format='NETCDF4')\n",
    "plant.description = 'Vegetation data for Dhara'\n",
    "\n",
    "# Set up dimension\n",
    "plant.createDimension('canopy', nl_can_in)  # canopy variable\n",
    "plant.createDimension('soil', nl_soil_in)   # soil variable\n",
    "plant.createDimension('time', num_steps_in) # time variable\n",
    "plant.createDimension('scalar', 1)          # Scalar variable\n",
    "\n",
    "\"\"\"\n",
    "Create variables in the netcdf file\n",
    "    var = netcdf.createVariable('Var_name', 'var_type', ('dimension_type'))\n",
    "\"\"\"\n",
    "LAI       = plant.createVariable('LeafAreaIndex', 'f8', ('time'))\n",
    "LAD       = plant.createVariable('LeafAreaDensity', 'f8', ('canopy'))\n",
    "rootfr    = plant.createVariable('RootFraction', 'f8', ('soil'))\n",
    "nl_can    = plant.createVariable('NumCanoyLayers', 'i4', ('scalar'))\n",
    "nl_soil   = plant.createVariable('NumSoilLayers', 'i4', ('scalar'))\n",
    "num_steps = plant.createVariable('NumTimeSteps', 'i4', ('scalar'))\n",
    "\n",
    "\n",
    "# Assign data to variables in NetCDF file\n",
    "LAI[:]       = LAI_in\n",
    "LAD[:]       = LAD_in\n",
    "rootfr[:]    = rootfr_in\n",
    "nl_can[:]    = nl_can_in\n",
    "nl_soil[:]   = nl_soil_in\n",
    "num_steps[:] = num_steps_in\n",
    "\n",
    "# Close the file\n",
    "plant.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OPTIONAL -- If you want to visualize canopy and root profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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OFzEEE/WDW6cRkdTxtzfRDTQ0t+Ovu0vw2XcX4XQJmDQ2As9lJWPcKD0A4JK4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85547c1358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ylad = np.linspace(1,nl_can_in,nl_can_in)\n",
    "ysoil = np.linspace(1,nl_soil_in,nl_soil_in)\n",
    "\n",
    "fig, ax = plt.subplots(ncols=2, nrows=1, figsize=(10,5))        \n",
    "ax[0].plot(LAD_in, ylad/nl_can_in)\n",
    "ax[0].set_xlabel('Leaf Area Density')\n",
    "ax[0].set_ylabel('z/h')\n",
    "ax[0].grid(True)\n",
    "ax[1].plot(rootfr_in, -ysoil/nl_soil_in)\n",
    "ax[1].set_xlabel('Root fraction')\n",
    "ax[1].set_ylabel('z/h')\n",
    "ax[1].grid(True)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### and Leaf Area Index data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XC5ocFqLgoteJaGiyYsch90q4IX3iEMOVcEQ+JWtoGThwINauXSvnJUiljpfU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\nOogAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8527f104a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting data\n",
    "fig, ax = plt.subplots(figsize=(9,4))\n",
    "ax.plot(LAI_in)\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('LAI')\n",
    "ax.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
